K-means Clustering and performance optimization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7bec2e1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\Machine-learning\\\\Assignments'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6244db60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\Machine-learning\\\\Assignments\\\\data_set'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir('F:\\\\Machine-learning\\\\Assignments\\\\data_set')\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8cdcddc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1    Male   19                  15                      39\n",
       "1           2    Male   21                  15                      81\n",
       "2           3  Female   20                  16                       6\n",
       "3           4  Female   23                  16                      77\n",
       "4           5  Female   31                  17                      40"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"mall customers.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bfb308ee",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   CustomerID  Gender  Age  Income  Score\n",
       "0           1    Male   19      15     39\n",
       "1           2    Male   21      15     81\n",
       "2           3  Female   20      16      6\n",
       "3           4  Female   23      16     77\n",
       "4           5  Female   31      17     40"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename({'Annual Income (k$)':'Income', 'Spending Score (1-100)':'Score'}, axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "935b932d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('CustomerID', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "04bc2327",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Income  Score\n",
       "0    Male   19      15     39\n",
       "1    Male   21      15     81\n",
       "2  Female   20      16      6\n",
       "3  Female   23      16     77\n",
       "4  Female   31      17     40"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4df33f6",
   "metadata": {},
   "source": [
    "# Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e9b6578",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "en = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "352542dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df.apply(en.fit_transform) is better for, if all columns are object or string types\n",
    "\n",
    "df.Gender = en.fit_transform(df[\"Gender\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f51ca58c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Income  Score\n",
       "0       1   19      15     39\n",
       "1       1   21      15     81\n",
       "2       0   20      16      6\n",
       "3       0   23      16     77\n",
       "4       0   31      17     40"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9fdb5af7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   Gender  200 non-null    int32\n",
      " 1   Age     200 non-null    int64\n",
      " 2   Income  200 non-null    int64\n",
      " 3   Score   200 non-null    int64\n",
      "dtypes: int32(1), int64(3)\n",
      "memory usage: 5.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9dceca8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.440000</td>\n",
       "      <td>38.850000</td>\n",
       "      <td>60.560000</td>\n",
       "      <td>50.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.497633</td>\n",
       "      <td>13.969007</td>\n",
       "      <td>26.264721</td>\n",
       "      <td>25.823522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>34.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gender         Age      Income       Score\n",
       "count  200.000000  200.000000  200.000000  200.000000\n",
       "mean     0.440000   38.850000   60.560000   50.200000\n",
       "std      0.497633   13.969007   26.264721   25.823522\n",
       "min      0.000000   18.000000   15.000000    1.000000\n",
       "25%      0.000000   28.750000   41.500000   34.750000\n",
       "50%      0.000000   36.000000   61.500000   50.000000\n",
       "75%      1.000000   49.000000   78.000000   73.000000\n",
       "max      1.000000   70.000000  137.000000   99.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#descriptive statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "024b9069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.96900733155888"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age.std() #standard deviation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60581d72",
   "metadata": {},
   "source": [
    "# Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2c56bea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2-D scatter')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# x = age, y = income\n",
    "plt.scatter(df.Age, df.Income)\n",
    "\n",
    "plt.xlabel(\"age\")\n",
    "plt.ylabel('income')\n",
    "plt.title('2-D scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bff7ea7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Score')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x = age, y = score\n",
    "plt.scatter(df.Age, df.Score, color='red', marker='+')\n",
    "\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c0cd4f26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Income-Score scatter')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lnscZ3zie6nMmNk9gA8nPV1pR93fywCQH9x+s2aZ73n29OE6lb5uS4FAceaORxWVof1RfSHstXbWULW/bErnPDzZe3KpSwo86tpkaZ5b7qHvePb06TqWvMwZo7/KDU23Au/Zgg4YfdOYsSvd5afsH4o4rU19IWVTu4fQd4UpaCfcqqoRfccrqUzI/X0l9YlHHSnck1dSVMZRcO2oE9SWHyh+UNCWItKWOpOM0p1L7LVuzLDKiaGBwYOqPdNy9Siq1b9+0nYVnLMz0zEXd34GhAcyspjlJ97y7erWm3rd9DI1kbTdMKiE2avdN2z/QqHRShr6QMhndMMrIRSNT/VA2aMw8YiaTz03WHBc1/iWp1N5MP0DU/T332nNZ+fGVuuc56tVxKqoxRGim3bBRCSFpf9pSR6PjytAXUjajG0ZrwlPXDayLPK5+/EtUCb9aMyXKuPure56fXq2pq8YQoZkIn0YlhKT9aUsdvVo6KYNKDZKEfufqZyRt1JuUW6/W1FVjiNBMu2FSCbFRCSJtqaNXSydFV1+DTFL9jKSNepNy68WaumoMEZopmdeUHGCqpJimBJG21NGrpZOiS+o/qlf/jOieSRmZe+OY7KIaGRnxsbHpUSOtiiohDs0e0he6T60bWJfYhFShZ0TKwszG3X0kbr+akiK0c2yDlF/cGILhecPMPGJmIZ6Ros6TVdR0STLVGEQaKHoNsqjpK2q6RDUGkZYVvQZZlNG39bWD/Xv3FyJdkp0yBpEUihx5UoTRt1Fjf+KUfVRwP1BUkkjJFWF8SyuRW1I8yhhESq4Ia4akrQVoDEc5qCkpA0VYSBEVoQ+kDJFbkp6iklJShIVIPH0/yqVRVJKaklIq6wppvbi6lBSPRnj3FjUlpVSEyI+senV1KSmmIkduSTaqMaRUhMiPrMpayxGRfCljSKkIkR9ZlbGWIyL5U1NSSkWI/MhK60D3PkXKSScoY8igbG2oWr+ht6kPSTpFTUk9TJEivU19SNIpqjH0uLLVciQ99SFJpyhjyEE324XVBt271IcknaKmpC6rtAvv2bUH/FC7cCcGnnXzXNJ9ZYyUk3LIpcZgZkcB1wAnEyya+E7gXuB6YDGwE3iruz+ZR/o6qZtz56c511SNYtcebNDwg86cRcWpWajGE6+MkXJSDnk1JV0FfM7d32JmM4HZwPuAbe7+ATO7FLgUuCSn9HVMN9uFG52rPqrFDwbzZhUlukVRN42pD0k6oesZg5nNAX4ZuADA3fcD+81sJXBmeNgm4Mv0YMbQzXbhRudKmkM/rhZTXYIfmj3Ec/uewycdGzSWrVnG6IbRtqU/S+0qqWahWodINnn0MZwI7AauNbM7zewaMzscONbdHwmPeRQ4NurNZrbGzMbMbGz37t1dSnL7dLNduNG5GtVS6vfX91kc+OkBfDKoZfhBZ+zqMbZevLVt6U9bu0rqS1E/i0h2eWQMM4DTgKvd/VTgpwTNRlM8mAs8cj5wd9/o7iPuPjJ//vyOJ7bdujm2oNG5GtVS6venWaVrfON4a4nm0Iyw0U9AunRVahZZYv01E61III8+hgeBB939jvD1DQQZw2NmtsDdHzGzBcDjOaStK7rZLpx0rqiR0RVRtZg0/SCVfopmRc3r32y6ktIbV+tQf4ZIDhmDuz9qZg+Y2Yvd/V5gOfDd8Gc18IHw35u7nbZ+UxPVkiIqKa7PopoNWqpzx7X7J9VKsqarUrOIXFls7jDrF6+fOv/+vfu7Fi0mUnR5RSX9PrA5jEj6AfAOgmatfzOzC4FdwFtzSltfyVJ7SaphVCxbs6zh5ySVzmNL+QZrd65Nna7qmkX9voGhAfY/vZ99T+ybOn8cjSKWfpRLxuDudwFRy8ppZE5BVUr4B545MFWzGDp8elTSwjMW1pTEo0r4Se3+zURtLV21lPu/ej/jG8fxg0FaTll9Ss15q2sn+/ceyhQa0Shi6UeaEkMaihrvMDR7iHM+WttpnradPqlP4LxPnpd5RtiJzRNs37R9qn/DDzrbN21n4RkLp2pE1edfZ+tSXbdGEUu/0pQYfaTZqJtGkT2Vz93yti2pIoCSVsNbumopp6w+ZaqvolL6B2LTnnWW0dh+kDBySzPRSr9TjaFPtBJ1E9cGv2fXnoZRRDC9hrDk7CWMXT027bglZy+JLP1/65pvcefH7uTg/oORaU9KX5TYyCmP78cQ6SepawxmNmxmL+5kYqRz0pb6o0rkcSVsG7RUYxvqawg7btsRedyO23ZEft7kgcmpTCEq7UmRUFG1ojmLYmosMdtF+k2qjMHMzgHuAj4Xvn65md3SwXRJmyW16zcaHRxXwvaD3jBqJ3LcQUIJP0sUUOXYpLETUaOcNSupSLK0NYY/B14JPAVTUUUndiRF0hFJ7fqNahNJJeykqJ24dvqkGkiWKKDKsUkl/ai+Bq1sJ5IsbcZwwN3ri3KtDXGVrkoqJTcaOZz03rh95/3reazduTbyj21SDSTq8waGBhicORh5/rj0RV1HtaWrlrJ251qumLwiNp0i/SptxvAdM/tNYNDMlpjZh4GvdTBd0mZxpWQAG4guwVdK5DXvJSjZV5fEs5a+k2oMUek899pzWfnxlVPbhucNM2N4BlveviWYUylMQ9znaiyCSDYWzFfX4CCz2cDlwK+Gmz4P/JW7/6yDaWtoZGTEx8amR7dIOkkRRUOzh6b9gY86Puq4RpLGEVzhV2ROcyUN93/1/shop5GLRto6HbhI2ZnZuLtHDTIGUoSrmtkgsNXdX0OQOUiPiIsoskGLzBRuXH3jtGagA88cYMvbtrDt8m2p1zmojJyO2t5MmpPGLEB8FJSIRGuYMbj7QTObNLM5Ef0MUmJxfQs+6ZE1haTonyzjIpL6GBppx0yqIpIs7QC3vcCEmX2RYP0EANz9DzqSKumKuHmJ0sw8GiXtbKSt1BjaMZOqVnATSZa283kL8KfAV4Dxqh8psbgIoP1P768Z05B2wjlInqm0opUaw5Kzl8Ruj9v37J5ntYKbSAapagzuvimcIvtF4aZ73b1xEVIKrWY9hiZmHo1kNCydz1kUU+pPMfI4rr9gfOM4s46aFblv8rnJmtdaZ0EkWaqMwczOBDYBOwEDTjCz1e7+lY6lTLpi2syjA+lmHo3lh2oNcf0OjdZPSJI0/1Gmmo36HURipW1K+jvgV939V9z9l4HXAx/qXLIkL3Ex/8PzhmvGFgzPG071eXEjj6NmUE0b0dQOGtsgEi9txjAULsMJgLt/H4gfaiqlFTeSecVVK2pGCr/srS9L/ZlR6ytHrZ+Qpt2/1TWlK+L6I0QkfcYwZmbXmNmZ4c8/AxpZVjJp1mNIO49QlrEB9aXzZsYiTH1Wm2ZA1dgGkXhpw1UvAt4FVMJTbwc2dCRF0hFZ1mNIsw50mugjyD67aiNxazlkpT4GkXhpM4YZwFXu/vcwNRr6sI6lStouqZRenwlU1neujiyqfEZlmw0YPhnRrGPhWIOEqKQs4xjq07J/7/6slx5JfQwi8dJmDNuAswgGugEMA18AfrETiZL2SztiOKpmcdM7bsLMalZQi5ViFbS04xii0tIOWntBJFnajGGWu1cyBdx9bzixnpREoxHDUyXziGMmD0xO2xZ7nhR9AGlrDGlWh6sYnjfMz576WcPO6ah5oESkVtrO55+a2WmVF2Y2ArQwCkq6LWlNhZoV3FqQtiSetsaQth+gEjUV2bRVf466eaBEZLq0GcNa4NNmdruZ3Q58Cvi9jqVK2i4p2ihLybxa/diGtCXxtGsupx1TUTlvmn4D9S2INJbYlGRmrwAecPdvmtlLgN8BziNY+/mHXUiftFFctFGjkvnA0EBNHwMcKqU3U/qOiyyqH1sQN0I67rxRx1dT34JIOo36GD5K0OkM8GrgfcDvAy8HNgJv6VjKpGvi+h8gKJFHRSW1MkNp3BiC+u1RczklnXfpqqXc/9X7Gd84HjRLGcw8fCb79+6ftupcO69HpNckruBmZtvd/ZTw938Cdrv7n4ev73L3l3cjkXG0glt7tGtltrTWDayLXjHc4IrJ5BXckkRdR1RtJ64GpE5p6RetruA2aGYz3P05YDmwJsN7pWC2Xrx1qjRtg8ayNcsY3TCauWReL2rcQ9J7G0VIpT7frj1TEU7D84b52ZM/m9YBHRVRFbVNM66KHNLoj/t1wP81sx8RRCHdDmBmJwEaOloiWy/eWtOu7wd96nUlc2jmj2KWEdUVafsY0pyvEsnU0lThIY2GFgkkZgzufqWZbQMWAF/wQ+1OAwR9DVIS4xuj11Ua3zjO6IbRzKX+iiwjqivS9jFUSxpn0S6KWBIJpFnz+esR277fmeRIpySNHWim1F/R1BrMGedKiuo7aEVcH4MilkQCaccxtJ2ZDZrZnWZ2a/j6RDO7w8zuM7PrwxXjuiLNrKNlF7eOgQ1aa7OdxpSyk0rfSWmJ0uw4i7hznHvtuZx64alNrQfRSD88S9L7cssYgHcD91S9/iDwIXc/CXgSuLAbiagZ9dvDawIvW7Msdnszpf6KpBHVcbKu+dyutv+h2UO8edObAZpeDyJJvzxL0vtyyRjM7HhgFLgmfG3Aa4EbwkM2Aed24tz1JbrPvvuzTZeWy2R0wygjF43UlJJHLhphdMNoU6X+irTrN9R8bsqRz1nSESVuhHQrNaQknfrcIlLNqLflFXK6HngvcGT4eh7wVBgWC/AgcFzUG81sDWHY7MKFCzOdNMtsnb0YoTK6YZTRDaPTtreyBjOkW7+hlfM1GtEcJWmEdCs1pCSd+tyiaaVPSsqh6zUGM3sj8Li7R4fJNODuG919xN1H5s+fn+m9Wdqq+ylCpZlSfzfPV3N8jCzzNrVSQ0rSqc8tmn6qGfWrPGoMZwBvMrOzgVnA84CrgKOqBtMdDzzU7hNnma2z3yJUmh3H0K3zVY6PG6WdZd6mVmtI3f7coumXmlE/63qNwd0vc/fj3X0xcD7wn+6+CvgSh+ZeWg3c3O5zZ52tU4qnHbWbTtWQul3zyku/1Iz6WeJcSR0/udmZwB+7+xvN7AUE03nPBe4E3ubuzya9P+tcSd2eE0ikF+l7VH6tzpXUUe7+ZeDL4e8/AF7ZyfO1OidQGTU7olla16v/9/34Peo3udYYWqXZVZOpZJcf/d9LkTWqMShjyKBRCbBIJcSJzRPcuPrGyEFjcxbNYe3Otd1PVAk1e0/XL14fGQ49PG+YmUfMLMQzIv2r0E1JZdIodrtIsd2VtHR6JHGv68QcUvue2Dc1E6zi/6Wo8pwSo1QaxW4XKba70XgNRY+kE3dPb1x9Y8ORvmn/jxX/L0WkjCGlRrHbRYrtTjpnL8bVd0rc/6Mf9IZzIEXNIZX1PCJ5UcaQUqPY7SLFdsed0wZNnZ8ZJN27RiX9qDENw/OGM59HJA/KGFJqNItoM7OMdkpcWt686c3KFDJoVOpvVNJfumopa3eu5YrJK1i7cy0rrlpRmGdEJIk6n1NqFLudJba709FLijNvj8r/V2x0V8aSflnvS5Gi7aQ7FK7aZYpvL59+vmf9fO29rFG4qpqSuqyVSBfJR7/MgRSlSNF20j1qSuqyRpEuoJj2Iur27LNFUaRoO+ke1Ri6rJVIF5FuK1K0nXSPMoYuazXSRaSbihRtJ92jpqQWZY3YaHeki3SGInECZY2kktYoY2hBs3PpVPb1w2pfZVSkea+KoF/7V/qZmpJa0ErERrciXSY2T7B+8XrWDaxj/eL1inxKQZE4kkUvfsdUY2hBqxEbnS6JqeTbHEXiSFq9+h1TjaEFRY/YUMm3OUW/r9V6sbRaJr36HVPG0IKiR2yo5Nucot/Xikppdc+uPeCHSqvKHLqnV79jakpqQdEiNuojaYbnDk8tClOtiCXfIinafa2ov7/79+6PLa3mndZ+MWfhnMiV+sr+HVPG0KKiRGxEtXUODA0wOHOQg/sPTh1XxJJvERXlvlZE3d84ZS+tlsnyK5f3ZHShmpJ6RFRb5+SBSWYeObMv5/jpNY1W5atW9tJqmfTqPFqqMfSI2DWGf7yP9/7ovV1OjbRb2lpAL5RWy6Zotct2UI2hR5Qpkkayi7uPw/OGe660KvlTjaFH9GpbpwTi7u+Kq1YUJiPQNCK9QxlDjyhqJI20R9Hvb68O9OpXWsFNRFq2fvH6yEip4XnDzDxiZiEzs37WaAU31RhEpGWxwQ9P7JsaS6NaRHmo81mkw/ph2oq0QQ69MF1EP1DGINJB/TJtRaMFqKppAF7xKWMQ6aBenWStXtRAr+F5w5HHKoS6+Lrex2BmJwD/AhwLOLDR3a8ys7nA9cBiYCfwVnd/stvpE2mnXp1kLUr9QK/6SCVQCHVZ5FFjeA54j7u/FDgdeJeZvRS4FNjm7kuAbeFrkVLrxMDDsvRZ9Op0Ef2g6zUGd38EeCT8/Wkzuwc4DlgJnBketgn4MnBJt9Mn0k5Lzl7C2NXTQ6qXnL2kqc8r23iBXpwuoh/k2sdgZouBU4E7gGPDTAPgUYKmJpFS23HbjkzbG+mXPgvJV24Zg5kdAfw7sNbdf1K9z4NRd5Ej78xsjZmNmdnY7t27u5BSkea1u4+hn/osJD+5ZAxmNkSQKWx29y3h5sfMbEG4fwHweNR73X2ju4+4+8j8+fO7k2CRJrW7j0GTJUo3+pi6njGYmQEfA+5x97+v2nULsDr8fTVwc7fTJtJu7V4mtCzLjkpndGtcTB41hjOAtwOvNbO7wp+zgQ8ArzOzHcBZ4WupU5aIFAksXbWUU1afgg0aADZonLL6lKY7ZMsW6aPntb261ceUR1TSfwEWs1vFngRli0iR4J5t37QdPxh0mflBZ/um7Sw8Y2FLmUMZ7ree1/brVh+TRj6XiCJSyqef71mZrr0sNZtu9TEpYygRRaSUT9RU1Enbe0lZntcyzWfVrT4mTbudQlFWppqzcE7kHxRFpBSXDdpUM1L99nbqxjOa9RxleV6TajZFa/Lq1oJNyhgaKFI7qZbvLJ+oTCFpezO68Yw2c46yPK9lqdlUdKOPSU1JMSptjlvetqXt7aRZ2zOn0vL2LcwYnhHMWlmCiBQJ7lGW7c1oti0/y3PYzDnKEkGlsSHTqcYQIWpWyHrNliaylrzqj9/3xD6GZg9x3ifPK9wXTKbrRqm5mRJv1uew2VJ1GSKoylKz6SbVGCJElY7qNVuayFryKlNkh0zX6VLzxOYJbCC6vyLpGc36XPVyqbosNZtuUo0hQpraQLOzY2YteZWt/VOm61SpuVLqj+uvSHpGsz5XvV6qLkPNpptUY4iQphTU7OyYWUtevVxS63Wdjo1vVLNNekazPlcqVfcXZQwR0qxf22yJPWscsubGKaduxMY3egaT9jfzXC1dtZS1O9dyxeQVrN25VplCD1PGEKGmdBSj2RJ71pKXSmr5arbU342+oUbPYNJ+PVeSxIKlD8ppZGTEx8amr47VTnHr1upL1PtauffrBtZFryhicMXkFR1LX9Z0Sn8ys3F3H4nbrxpDAypZ9a9Gpf6k2kQ3+obqZ26dOoeeUWmRopJSUMRCf0qK3Gk0DqAbUTz1M7dWn0PPq7RCNQaRGEml/ka1iW7UNDXGRTpFNQaRGEvOXsLY1dP7sJacvYSxj0T3bVXXMjpd09QYF+kUZQwiMeLGAey4bUchZg4tQhqKoCizH/cSNSWJxEgqkRdhfEkR0pC3Mq2lUCbKGERixJW8h+cOT7XvVyKC8ogEUsSc+lk6RU1JIjGiIosGhgbY//R+9j2xDwjWVcgzEqjfI+bUz9IZfZ0xRLVNQudXR5JyiFota//eQ5lCRVFX++oH6mfpjL7NGKLi0G96x02YGQf3H5zaltdqbVIM9SXydQPrIo9TCTUfvT7ra176NmOIapucPDA57bhmSoOKkuhdcSXU4bnDrF+8vql7rueled1aA7nf9G3GkKWEl+XYIq0RLe0XN7bh2T3PTjUxZbnnel5a1+/9LJ3Qt1FJWdogsxzbifV3Oz2vv6QXN7Zh8rna2mbayBhF1UgR9W2NIS7ipLqPAbK3V7Z7/V1AJcoCaXdNU1E1UkR9W2OIigE/99pzWfnxlVPrMNigTZXe0pbSm5lVM6nUqBJlsbS7pqkV+qSI+rbGAMltk82W0puJkmim1KgSZT7aXdNUVI0UUd/WGJK0UkpvZjRqUqlRJcpiaVjTzDgCWaOXpYi0gluEbqy+VS1ppTBAK8iJSFs1WsGtr5uS4nR7NGWaWGzFaZfX1ou3Mr5xHD/o2KCxbM0yRjeM5p0skViqMUTQOs/SLlsv3ho57mHkohFlDpKbUq35bGZvMLN7zew+M7s0r3So3VfaZXzjeKbtIkVQmKYkMxsE/gl4HfAg8E0zu8Xdv5tHejSaUtqhej3mNNtFiqBINYZXAve5+w/cfT/wKWBlzmkSaUllvYa020WKoEgZw3HAA1WvHwy31TCzNWY2ZmZju3fv7lriRJqxbM2yTNtFiqBIGUMq7r7R3UfcfWT+/Pl5J0ck0eiGUUYuGpmqIdigqeNZCq8wfQzAQ8AJVa+PD7eJlNrohlFlBFIqRaoxfBNYYmYnmtlM4HzglpzTJCLSdwpTY3D358zs94DPA4PAx939OzknS0Sk7xQmYwBw99uA2/JOh4hIPytSU5KIiBSAMgYREalR6rmSzGw3sKuLpzwG+FEXz9cpuo7i6IVrAF1H0TS6jkXuHhvvX+qModvMbCxp4qmy0HUURy9cA+g6iqbV61BTkoiI1FDGICIiNZQxZLMx7wS0ia6jOHrhGkDXUTQtXYf6GEREpIZqDCIiUkMZg4iI1FDGEMHMTjCzL5nZd83sO2b27nD7XDP7opntCP89Ou+0pmFmg2Z2p5ndGr4+0czuCJdQvT6ctLDQzOwoM7vBzL5nZveY2avLeD/M7A/DZ+puM7vOzGaV4X6Y2cfN7HEzu7tqW+T/vwX+Ibyeb5vZafmlvFbMdfxN+Fx928xuNLOjqvZdFl7HvWb2+lwSHSHqOqr2vcfM3MyOCV9nvh/KGKI9B7zH3V8KnA68y8xeClwKbHP3JcC28HUZvBu4p+r1B4EPuftJwJPAhbmkKpurgM+5+0uAUwiup1T3w8yOA/4AGHH3kwkmizyfctyPTwBvqNsW9/+/AlgS/qwBru5SGtP4BNOv44vAye7+C8D3gcsAwu/8+cDLwvdsCJcgLoJPMP06MLMTgF8F7q/anP1+uLt+GvwANxOsRX0vsCDctgC4N++0pUj78QRf2tcCtwJGMCJyRrj/1cDn805ng2uYA/yQMFiianup7geHVimcSzCB5a3A68tyP4DFwN2N/v+BjwK/EXVcEX7qr6Nu35uBzeHvlwGXVe37PPDqvNOfdB3ADQQFp53AMc3eD9UYGjCzxcCpwB3Ase7+SLjrUeDYvNKVwXrgvcBk+Hoe8JS7Pxe+jlxCtWBOBHYD14ZNYteY2eGU7H64+0PA3xKU5h4B9gDjlO9+VMT9/6dapreg3gl8Nvy9VNdhZiuBh9x9e92uzNehjCGBmR0B/Duw1t1/Ur3Pg6y30LG+ZvZG4HF3H887LS2aAZwGXO3upwI/pa7ZqCT342hgJUFG93zgcCKaA8qoDP//jZjZ5QTNyJvzTktWZjYbeB/wZ+34PGUMMcxsiCBT2OzuW8LNj5nZgnD/AuDxvNKX0hnAm8xsJ/Apguakq4CjzKyyFkcZllB9EHjQ3e8IX99AkFGU7X6cBfzQ3Xe7+wFgC8E9Ktv9qIj7/y/dMr1mdgHwRmBVmMlBua7jhQQFju3h9/144Ftm9vM0cR3KGCKYmQEfA+5x97+v2nULsDr8fTVB30Nhuftl7n68uy8m6ET7T3dfBXwJeEt4WBmu41HgATN7cbhpOfBdSnY/CJqQTjez2eEzVrmOUt2PKnH//7cAvxVGw5wO7KlqciocM3sDQXPrm9z9mapdtwDnm9lhZnYiQeftN/JIYyPuPuHuP+fui8Pv+4PAaeF3J/v9yLsDpYg/wC8RVIu/DdwV/pxN0D6/DdgB/AcwN++0ZrimM4Fbw99fQPCA3wd8Gjgs7/SlSP/LgbHwntwEHF3G+wGsA74H3A18EjisDPcDuI6gX+RA+Efnwrj/f4IAh38C/huYIIjCyv0aEq7jPoI2+Mp3/SNVx18eXse9wIq80590HXX7d3Ko8znz/dCUGCIiUkNNSSIiUkMZg4iI1FDGICIiNZQxiIhIDWUMIiJSQxmDSB0z25t3GkTypIxBRERqKGMQiWFmZ5rZl6vWgdgcjljGzF5hZl8zs+1m9g0zOzJcW+FaM5sIJ/t7TXjsBWZ2U7hmwU4z+z0z+6PwmK+b2dzwuBea2efMbNzMbjezl+R5/dK/ZjQ+RKSvnUowH//DwFeBM8zsG8D1wK+7+zfN7HnAPoJ1L9zdl4Z/1L9gZi8KP+fk8LNmEYy0vcTdTzWzDwG/RTAL7kbgd919h5m9CthAML+VSFcpYxBJ9g13fxDAzO4imAN/D/CIu38TwMOZd83sl4APh9u+Z2a7gErG8CV3fxp42sz2AJ8Jt08AvxDO5PuLwKfDSgkE02WIdJ0yBpFkz1b9fpDmvzPVnzNZ9Xoy/MwBgnUZXt7k54u0jfoYRLK7F1hgZq8ACPsXZgC3A6vCbS8CFobHNhTWOn5oZr8Wvt/M7JROJF6kEWUMIhm5+37g14EPm9l2gjWDZxH0CQyY2QRBH8QF7v5s/CdNswq4MPzM7xAs6iPSdZpdVUREaqjGICIiNZQxiIhIDWUMIiJSQxmDiIjUUMYgIiI1lDGIiEgNZQwiIlLj/wPtW4obbJh9BwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x = income, y = score\n",
    "plt.scatter(df.Income, df.Score, color='purple')\n",
    "\n",
    "plt.xlabel('Income')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Income-Score scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82aa85de",
   "metadata": {},
   "source": [
    "# Pairplot visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7771fc74",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4307bda0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x175aa69d0d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,10))\n",
    "sns.pairplot(df[['Age','Income','Score']])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc9c73b3",
   "metadata": {},
   "source": [
    "# Now, K-means Clustering Algorithm for cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d561ff59",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "kmeans = KMeans()    # by default is split into 8 cluster & this is not good taking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "905ef495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(df[['Income', 'Score']].values)  # for 2d visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "227057f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = kmeans.predict(df[['Income', 'Score']].values)  # to avoid warning, just call values field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0c8ce949",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 7, 5, 7, 5, 7, 5, 7, 5, 7, 5, 7, 5, 7, 5, 7, 5, 7, 5, 7, 5, 7,\n",
       "       5, 7, 5, 7, 5, 3, 5, 7, 5, 7, 5, 7, 5, 7, 5, 7, 5, 7, 3, 7, 3, 3,\n",
       "       5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 0, 3, 0, 3, 3, 0, 0, 3, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 2, 4, 0, 4, 2, 4, 2, 4,\n",
       "       0, 4, 2, 4, 2, 4, 2, 4, 2, 4, 0, 4, 2, 4, 0, 4, 2, 4, 2, 4, 2, 4,\n",
       "       2, 4, 2, 4, 2, 4, 0, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4,\n",
       "       2, 4, 2, 4, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1,\n",
       "       6, 1])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b382c859",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['clusters_number'] = pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c2c86bf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Score</th>\n",
       "      <th>clusters_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>120</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>62</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>57</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>101</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Age  Income  Score  clusters_number\n",
       "194       0   47     120     16                6\n",
       "103       1   26      62     55                0\n",
       "87        0   22      57     55                0\n",
       "186       0   54     101     24                6\n",
       "190       0   34     103     23                6"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "19b2d22f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 62.58333333,  47.29166667],\n",
       "       [109.7       ,  82.        ],\n",
       "       [ 79.73913043,  13.39130435],\n",
       "       [ 45.51282051,  51.51282051],\n",
       "       [ 78.55172414,  82.17241379],\n",
       "       [ 25.14285714,  19.52380952],\n",
       "       [109.7       ,  22.        ],\n",
       "       [ 24.95      ,  81.        ]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids = kmeans.cluster_centers_    # to see all centroid point of all cluster\n",
    "centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "caeb6c64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict a point\n",
    "kmeans.predict([[46, 55]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34580d20",
   "metadata": {},
   "source": [
    "### clusters with default parameter visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4b1b09e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x175ab287280>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl0 = df[df.clusters_number == 0 ]\n",
    "cl1 = df[df.clusters_number == 1 ]\n",
    "cl2 = df[df.clusters_number == 2 ]\n",
    "cl3 = df[df.clusters_number == 3 ]\n",
    "cl4 = df[df.clusters_number == 4 ]\n",
    "cl5 = df[df.clusters_number == 5 ]\n",
    "cl6 = df[df.clusters_number == 6 ]\n",
    "cl7 = df[df.clusters_number == 7 ]\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.scatter(cl0['Score'], cl0['Income'], color='green', label='cluster_0')\n",
    "plt.scatter(cl1['Score'], cl1['Income'], color='red',label='Cluster_1')\n",
    "plt.scatter(cl2['Score'], cl2['Income'], color='black',label='Cluster_2')\n",
    "plt.scatter(cl3['Score'], cl3['Income'], color='orange',label='Cluster_3')\n",
    "plt.scatter(cl4['Score'], cl4['Income'], color='cyan',label='Cluster_4')\n",
    "plt.scatter(cl5['Score'], cl5['Income'], color='pink',label='Cluster_5')\n",
    "plt.scatter(cl6['Score'], cl6['Income'], color='blue',label='Cluster_6')\n",
    "plt.scatter(cl7['Score'], cl7['Income'], color='yellow',label='Cluster_7')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0a9e14f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next step to optimize cluster parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b1577e6",
   "metadata": {},
   "source": [
    "# Tuning to k-means cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "55f6eb53",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4b73cb4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the Elbow method\n",
    "wcss = []\n",
    "clusters = range(1, 15)\n",
    "\n",
    "for k in range(1, 15):\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(df[['Income', 'Score']])\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2cad99dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[269981.28,\n",
       " 181363.59595959596,\n",
       " 106348.37306211119,\n",
       " 73679.78903948834,\n",
       " 44448.45544793371,\n",
       " 37233.81451071001,\n",
       " 30241.343617936585,\n",
       " 24995.969781135962,\n",
       " 21818.114588452183,\n",
       " 19669.71099830122,\n",
       " 17595.28888108518,\n",
       " 15823.742746763313,\n",
       " 14320.918182339483,\n",
       " 13133.432800532799]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bef45600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Elbow method')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MrLgio8XelNQfQNI/SbqpciEvq526OvjoI7jxxrIjMTNrmyLJ5Z8j4j1JOwN7k+YLu7C2YRnAF74Am28Ov/td2ZGYmbVNkeSyPH8eBFwcEX8G1qxdSFZPgokT4eGH4Yknyo7GzKy4IsllrqTfAeOB2yStVfA6q4Ljjktv7bv2YmadSZEkcTRp2vv9ImIhMJgCyxxbdQwaBOPHw3/9F7z3XtnRmJkV09J7LvXjk3oD9wJv5/dePqbgYmFWHRMnwvvvw3//d9mRmJkV01LNpf5/ZfUrT1a+6+Lk0o523BG22QYuughaWSHBzKxDaDa5RMSX8ucmEfEPbV2J0qqnvmP/iSfg0UdbPd3MrHQtNYtt19LWnkEaHHtsWqnyoovKjsTMrHUtLaR7Xv7sDYwBngQEbE1qFvtCbUOzSv37w5e/DFddBeedlzr6zcw6qpaaxfaIiD2AN4DtImJMRGwPbAvMba8ArcHEifDhhynBmJl1ZEWGIm8WEbPqv0TEbOCztQvJmrPttrDDDu7YN7OOr0hyeUrSpZJ2z9slwFO1DsyaNnEiPPssPPBA2ZGYmTWvSHI5AXgaOC1vz+QyK8H48bDOOn5j38w6tiLLHH8UERdExGF5uyAiPmqP4OyT+vRJU8LccAPMn192NGZmTfMcYZ3QKafAkiVwxRVlR2Jm1jQnl05oyy1hl11S09iKFWVHY2b2SU4undTEifDCCzBtWtmRmJl9UrMvUUq6FWh2wGtEHFKTiKyQI46A005LtZd99ik7GjOzlbVUc/kV6S39l4APgUvy9j7wQu1Ds5astRYcfzz88Y/w+utlR2NmtrKW3tC/LyLuA3aKiPERcWvevgzs0n4hWnNOPhmWL4fLLy87EjOzlRXpc+kr6e+zIEvaBOjb2kWSLpf0lqTZFWU/lTRX0hN5O7Di2A8lzZH0nKT9Ksr3z2VzJJ1ZGYekh3P5dZLWzOVr5e9z8vERBX7GTmnUKNh7b7j44pRkzMw6iiLJ5XTgXkn3SroPuAf4ToHrrgD2b6L8gogYnbfbACRtAdQBW+Zr/lNST0k9gf8ADgC2AI7J5wL8It9rU+Ad4MRcfiLwTi6/IJ/XZU2cCK++CrffXnYkZmYNirxEeQcwivR2/rdJc43dWeC6+4EFBeMYB1wbER9HxEvAHGCHvM2JiBcjYglwLTBOkoA9gRvy9ZOAQyvuNSnv3wDslc/vkg45BNZbz1Pxm1nH0mpykdQH+D7wzYh4EthY0pdW45nflPRUbjarnzh+Q+DVinNey2XNla8LLIyIZY3KV7pXPv5uPr9LWmMNOPFEuO02eOWVsqMxM0uKNIv9HlhCw/otc4F/XcXnXQiMBEaTpvI/r8Wza0zSyZJmSJoxvxPPpXLSSenz0kvLjcPMrF6R5DIyIn4JLAWIiMWkRcPaLCLmRcTyiFhBGta8Qz40FxhecepGuay58reBgZJ6NSpf6V75+Dr5/KbiuTivUzNm6NChq/IjdQif/jQceGBKLkuXlh2NmVmx5LJE0trkFyoljQQ+XpWHSVq/4uthQP1IsslAXR7ptQmpj+cR4FFgVB4Ztiap039yRARpYMGR+foJwC0V95qQ948E7s7nd2kTJ8Kbb8LkyWVHYmbW8jLH9c4C7gCGS7oa2Ak4vrWLJF0D7A4MkfRavs/ukkaTEtXLwCkAEfG0pOtJ0/kvA06NiOX5Pt8E7gR6ApdHxNP5EWcA10r6V+Bx4LJcfhlwlaQ5pAEFdQV+xk7vgANg+PDUsX/EEWVHY2bdnVr6pV5SD9Jv/9OAsaTmsOkR8bf2Ca/9jBkzJmbMmFF2GKvl7LPhJz+B55+HTTctOxoz6w4kzYyIMY3LW2wWy30jP4iItyPizxHxp66YWLqKE0+Enj3TS5VmZmUq0udyl6R/lDRc0uD6reaRWZttsAGMG5emg/l4lXrFzMyqo0hyGQ+cCtwPzMxb524/6sImToS334Ybbyw7EjPrzlrt0I+ITdojEKuOvfaCkSNTx/6Xv1x2NGbWXRUZLYakrUhze/WuL4uIK2sVlK26Hj3SbMlnnAHPPANbbNH6NWZm1VZk+pezgH/P2x7ALwEvFNaBnXBCmhbmd78rOxIz666K9LkcCewFvBkRJwDbkN56tw5q6FA48kiYNAkWLy47GjPrjooklw/zkORlkgYAb7HylCzWAU2cCO++C9ddV3YkZtYdFUkuMyQNJM0FNhN4DPjfWgZlq2+XXeCzn/VU/GZWjiLruXwjIhZGxEXAPsCE3DxmHZgEp5wCjzwCjz9edjRm1t0U6dDftX4DNibNRrxr7UOz1XXccdC7tzv2zaz9FRmK/P2K/d6kafJnklaCtA5s0CCoq4Orr4Zzz4X+/cuOyMy6iyLNYgdXbPsAW5HWrLdOYOJEeP/9lGDMzNpLkQ79xl4DPlvtQKw2dtgBRo9OHftdf1UbM+soWm0Wk/Tv5IXCSMloNGnEmHUC9R37X/86PPwwjB1bdkRm1h0UGopMw4SV/wucERFfqWlUVlXHHgv9+rlj38zaT5GJKye1RyBWO/37pwQzaRKcf37q6Dczq6UiQ5FnSXqqiW2WpKfaI0hbfaecAh99BFd6ulEzawdFhiLfnj+vyp/H5s8Lqx+O1cq228KOO6aO/W9/O/XFmJnVSpE+l30i4gcRMStvZwL7RsQrEfFKrQO06pk4Ef7yF7j//rIjMbOurkhykaSdKr58seB11sEcfTSss47nGzOz2ivSLHYicLmkdQCRXqD8Wk2jspro0wcmTIALL4S33oJPfarsiMysqyryhv7MiNiGtI7L1hExOiL8nksndcopsHQpXHFF2ZGYWVdWZLTYaXkdl0XAeZIek7Rv7UOzWthiC9h11/TOy4oVZUdjZl1Vkb6Tr0XEImBfYF3gq8A5NY3KamriRHjxRbjrrrIjMbOuqlCHfv48ELgyIp6uKLNO6PDDYcgQd+ybWe0USS4zJU0hJZc7JfUH3KDSia21FpxwAkyeDK+/XnY0ZtYVFUkuJwJnAp+PiMXAmoBXouzkTj4Zli+HSy4pOxIz64qKjBZbERGPRcTC/P3tiPC0L53cppvCwQfDz38O06aVHY2ZdTV+GbIbmzQJNtsMxo1L0/GbmVVLs8lF0iarc2NJl0t6S9LsirLBkqZKej5/DsrlkvQbSXPypJjbVVwzIZ//vKQJFeXb58kz5+Rr1dIz7JMGDYI774Rhw+CAA2D27NavMTMroqWayw0Akla10eQKYP9GZWcC0yJiFDAtfwc4ABiVt5PJk2JKGgycBewI7ACcVZEsLgROqrhu/1aeYU1Yf/00JLl3b9h33zRE2cxsdbWUXHpI+hHwGUnfbby1duOIuB9Y0Kh4HFC/Pswk4NCK8isjmQ4MlLQ+sB8wNSIWRMQ7wFRg/3xsQERMj4gArmx0r6aeYc3YZBOYOhU+/hj22QfeeKPsiMyss2spudQBy0nzj/VvYlsVwyKi/n9dbwLD8v6GwKsV572Wy1oqf62J8pae8QmSTpY0Q9KM+fPnr8KP03VsuSXcdhvMm5dqMAsa/1pgZtYGzU5cGRHPAb+Q9FRE3N7ceasqIkJSVPu+bXlGRFwMXAwwZsyYmsbSGey4I9xyCxx4IBx0UKrN9OtXdlRm1hkVGS32P5LOr/8NX9J5eYbkVTEvN2mRP9/K5XOB4RXnbZTLWirfqInylp5hBey1F1x7LTzySHqT/+OPy47IzDqjIsnlcuA94Oi8LQJ+v4rPmwzUj/iaANxSUX5cHjU2Fng3N23dCewraVDuyN8XuDMfWyRpbB4ldlyjezX1DCvosMPgsstSzeXYY9PLlmZmbVFkPZeREXFExfd/kfREaxdJugbYHRgi6TXSqK9zgOslnQi8QkpWALeRppeZAywmzwAQEQsknQ08ms/7WUTU9wZ8gzQibW3SUsz1TXfNPcPa4PjjYeFCOP30NE3/JZd4aWQzK65IcvlQ0s4R8SBAXpXyw9Yuiohjmjm0VxPnBnBqM/e5nFR7alw+A9iqifK3m3qGtd13vpM69s8+GwYOhHPPdYIxs2KKJJeJwJUV/Szv0NDsZF3cv/wLvPMOnHceDB4MP/pR2RGZWWfQanKJiCeBbfKCYeS1XaybkODXv04J5sc/Tm/1f/3rZUdlZh1dkZoL4KTSnfXoAb//PSxaBKeemprIjmmu0dPMDE9caQWtsQZcd11aIvm44+DPfy47IjPryJxcrLC1104LjG29NRx5JDzwQNkRmVlH1WqzmKSewEHAiMrzI+L82oVlHdWAAXDHHakG86Uvwb33wrbblh2VmXU0RWoutwLHA+uy+nOLWRcwdChMmZL6XvbbD557ruyIzKyjKdKhv1FEbF3zSKxTGT48vcG/yy5pJuWHHkplZmZQrOZyu6R9ax6JdTqf+UxabOzdd1OCecuzuJlZViS5TAdulvShpEWS3pPkYckGwOjRaeTYX/8K+++fEo2ZWZHkcj7wBaBPRAyIiP4RMaDGcVknsvPOcMMNMGsWHHIIfNjq5EBm1tUVSS6vArPz/F9mTTrwQLjqqjQ8+aijYOnSsiMyszIV6dB/EbhX0u3A31f38FBka6yuLjWLTZyYZlW+6qr0dr+ZdT9FkstLeVszb2bNOuWUNA/ZD3+Yhir/9reeSdmsOyoyceW/tEcg1nWccUaaqr9+iv7f/MY1GLPupsgb+vcAn+hviYg9axKRdXoS/OIXEAG/+lXq4L/4YujZs+zIzKy9FGkW+8eK/d7AEcCy2oRjXYUEv/wl9OkDP/tZSjCTJqUJMM2s6yvSLDazUdFDkh6pUTzWhUhpsbG11059MB99BNdeC2u6586syyvSLDa44msPYHtgnWZON/uEM89MNZjTToNDD4Ubb0wJx8y6riLNYjNJfS4iNYe9BJxYy6Cs6/n2t1NCOeUUOOigNHV/v35lR2VmtVKkWWyT9gjEur6TTkoJ5vjj02zKt90G67gObNYltTpAVNJRkvrn/X+SdJOk7WofmnVFX/lKWtHykUdgr73g7bfLjsjMaqHI2wf/HBHvSdoZ2Bu4DLiwtmFZV3bEEfDHP8Ls2bDHHjBvXtkRmVm1FUkuy/PnQcDFEfFn/Ka+raaDDkqzKb/wAuy2G8ydW3ZEZlZNRZLLXEm/A8YDt0laq+B1Zi3aa6+0Hszrr6dlk19+ueyIzKxaiiSJo4E7gf0iYiEwGPh+LYOy7mPnnWHatDQf2a67wvPPlx2RmVVDq8klIhZHxE0R8Xz+/kZETKl9aNZdfP7zcM896S3+XXeFp58uOyIzW11u3rIOYZtt4P7701v9u+0Gjz9edkRmtjqcXKzD+OxnU4Lp2zeNIps+veyIzGxVlZJcJL0saZakJyTNyGWDJU2V9Hz+HJTLJek3kuZIeqryHRtJE/L5z0uaUFG+fb7/nHytVxTpJDbdNCWYIUNgn33gvvvKjsjMVkWZNZc9ImJ0RIzJ388EpkXEKGBa/g5wADAqbyeT37HJc56dBewI7ACcVZ+Q8jknVVy3f+1/HKuWT386JZjhw+GAA2CKe/jMOp2O1Cw2DpiU9ycBh1aUXxnJdGCgpPWB/YCpEbEgIt4BpgL752MDImJ6RARwZcW9rJPYYINUa/nMZ+Dgg+HWW8uOyMzaoqzkEsAUSTMlnZzLhkXEG3n/TWBY3t8QeLXi2tdyWUvlrzVRbp3M0KFpFNno0XD44XD99WVHZGZFFZkVuRZ2joi5kj4FTJX0l8qDERGSPrH6ZbXlxHYywMYbb1zrx9kqGDQIpk5Nb/Qfc0xaE+a448qOysxaU0rNJSLm5s+3gJtJfSbzcpMW+fOtfPpcYHjF5RvlspbKN2qivKk4Lo6IMRExZujQoav7Y1mNDBgAd9wBe+4JEybA735XdkRm1pp2Ty6S+lbMstwX2BeYDUwG6kd8TQBuyfuTgePyqLGxwLu5+exOYF9Jg3JH/r7AnfnYIklj8yix4yruZZ1U376p3+Wgg2DiRPi3fys7IjNrSRnNYsOAm/Po4F7Af0fEHZIeBa6XdCLwCmnaGYDbgAOBOcBi4ASAiFgg6Wzg0XzezyJiQd7/BnAFsDZwe96sk+vdG266CY49Fk4/Pc1LtvvuaQqZMWNgrbXKjtDM6ikNqLIxY8bEjBkzyg7DCli2DH7yk5RonnsulfXuDTvskBLNLrvAF7+YmtPMrLYkzax4paSh3MklcXLpnObPhwcfhAceSNvjj8Py5dCjR5pSpj7Z7LILrLde2dGadT1OLq1wcuka3n8/TRvzwAMp6UyfDosXp2MjRzYkml12SbMBeO4Gs9Xj5NIKJ5euaelSeOyxhtrNgw82LK08bFhDzWbnnVNNp1dZg/PNOiknl1Y4uXQPK1akfpr6ZrQHH2xYpKxfv9RXU59wdtgB+vQpNVyzDs/JpRVOLt3Xa681JJoHHoDZsyEi1WK23TYlnJ12StsGG5QdrVnH4uTSCicXq/fOO/A//wMPPZQ+H3kkLWQGMGJEQ6LZaSfYckvo2bPUcM1K5eTSCicXa86SJfDEEynZ1G9vvpmODRgAX/hCQ+1mxx1T85pZd+Hk0gonFysqAl56qaF289BDDU1pPXumgQGVtZuNNmr9nmadlZNLK5xcbHUsXJiGPdcnm4cfbhgCPXz4ysnmc5/zqDTrOpxcWuHkYtW0bBk8+eTKTWlz8/Sp/frB1lvDVlutvHnuVOuMnFxa4eRitRQBr77aMEhg1qy0LVjQcM6nPpWSzOc+15BwttwS+vcvL26z1jSXXFw5N2sHEmy8cdqOOSaVRcC8eam/Ztas9Dl7Nlx6KXzwQcO1n/70yjWcz30ONtsszadm1lE5uZiVRErzna23Huy9d0P5ihXwyisrJ5zZs2HKlDTjAKSBA6NGfbJpbeRI9+dYx+C/hmYdTI8esMkmaTvkkIbypUvh+edXTjpPPAE33phqQZCWHRg5EjbcMG0bbNCwX/992DC/m2O15+Ri1kmssQZssUXaxo9vKF+8GJ59tqF57cUX0+CBZ55J7+MsX77yfXr2TLWl5pJP/b6XLLDV4eRi1sn16QPbb5+2xpYvh7feSsmmfnv99Yb9//s/uPfeNJS6sX79Vk42G2yQtiFDYPDglbeBA10bspU5uZh1YT17wvrrp23MJ8bzNPjgg4akU5l86r8/8ED6rO/zacrAgSnRrLvuJ5NPc9ugQalGZl2Pk4uZ0bdvGiAwalTz56xYkYZOV25vv/3JsvrthRfS5zvvNPQJNaV//4aENGhQ85+Ny/r183o8HZmTi5kV0qNHahIbMqRt161YAe++23wSqk9Sb7+dEtHcuelzwYKWa0q9ejWddJr7HDgQ1lknbU5MtefkYmY11aNHQxIYObL4dRFpsEJ97ae1z7feSmv1vPNO6kNqqbbUo0casFCfbBpvLR2r3/r3T/expjm5mFmHJKXmur590/xsbbF8OSxa9MkE9O67aVu0qGG/fqsfYVf/fdmy1uPr378hEQ0YkGpE/funrbX9xmW9e3et2pSTi5l1OT17NtSWVkVEWsOnqSTU3Pb++/Dee2ngQ/3+e++13LTXOOaWElGfPqu+rb12+w+ccHIxM2tEavgf83rrrd69lixJSaYy4bS2X1n29tvp88MPUzPh4sXFE1alNdZYOdlUJp9zzklrEVWTk4uZWQ2tuWYaDbfuutW759KlKyeb1d1q0Rzn5GJm1smssUbaOvIsCh7rYGZmVefkYmZmVefkYmZmVefkYmZmVefkYmZmVefkYmZmVefkYmZmVefkYmZmVadoaerQbkTSfOCVsuNoxhDgb2UHsQo6a9zg2Mvi2MuxOrF/OiKGNi50cukEJM2IiBbWEeyYOmvc4NjL4tjLUYvY3SxmZmZV5+RiZmZV5+TSOVxcdgCrqLPGDY69LI69HFWP3X0uZmZWda65mJlZ1Tm5mJlZ1Tm5dFCShku6R9Izkp6WdFrZMbWVpJ6SHpf0p7JjaQtJAyXdIOkvkp6V9IWyYypK0un578tsSddI6l12TM2RdLmktyTNrigbLGmqpOfz56AyY2xKM3Gfm/++PCXpZkkDSwyxWU3FXnHse5JC0pBqPMvJpeNaBnwvIrYAxgKnStqi5Jja6jTg2bKDWAW/Bu6IiM2BbegkP4OkDYFvA2MiYiugJ1BXblQtugLYv1HZmcC0iBgFTMvfO5or+GTcU4GtImJr4P+AH7Z3UAVdwSdjR9JwYF/gr9V6kJNLBxURb0TEY3n/PdL/4DYsN6riJG0EHARcWnYsbSFpHWBX4DKAiFgSEQtLDaptegFrS+oF9AFeLzmeZkXE/cCCRsXjgEl5fxJwaHvGVERTcUfElIhYlr9OBzZq98AKaObPHOAC4AdA1UZ4Obl0ApJGANsCD5ccSlv8G+kv64qS42irTYD5wO9zk96lkvqWHVQRETEX+BXpt883gHcjYkq5UbXZsIh4I++/CQwrM5hV9DXg9rKDKErSOGBuRDxZzfs6uXRwkvoBNwLfiYhFZcdThKQvAW9FxMyyY1kFvYDtgAsjYlvgAzpm08wn5P6JcaQEuQHQV9JXyo1q1UV6T6JTvSsh6cekJu2ry46lCEl9gB8BP6n2vZ1cOjBJa5ASy9URcVPZ8bTBTsAhkl4GrgX2lPRf5YZU2GvAaxFRX0u8gZRsOoO9gZciYn5ELAVuAr5YckxtNU/S+gD5862S4ylM0vHAl4Bjo/O8QDiS9MvIk/nf60bAY5LWW90bO7l0UJJEavd/NiLOLzuetoiIH0bERhExgtShfHdEdIrfoCPiTeBVSZvlor2AZ0oMqS3+CoyV1Cf//dmLTjIYocJkYELenwDcUmIshUnan9QMfEhELC47nqIiYlZEfCoiRuR/r68B2+V/B6vFyaXj2gn4Kum3/ifydmDZQXUT3wKulvQUMBr4ebnhFJNrWzcAjwGzSP++O+yUJJKuAf4X2EzSa5JOBM4B9pH0PKkmdk6ZMTalmbh/C/QHpuZ/qxeVGmQzmom9Ns/qPLU3MzPrLFxzMTOzqnNyMTOzqnNyMTOzqnNyMTOzqnNyMTOzqnNyMTOzqnNyMasCSfdKGtOOzzs3T61/bhuve38Vn3doJ5yV20rUq+wAzLo7Sb0qZtQt6mRgcEQsr0VMTTgU+BNtmK1gFX8u6yJcc7FuRdKIvADYJfk3/ymS1q6seUgakudZQtLxkv6YF656WdI3JX03z5g8XdLgitt/Nb+dPVvSDvn6vnmBpkfyNeMq7jtZ0t2kdUuailW5hjJb0ixJ43P5ZKAfMLO+rIlrh+VFq57M2xcbHd9dFYu4SfptnhsLSecoLVL3lKRf5WsPAc7NP9/IvN0haaakByRtnq+9QtJFkh4GftnG/zzWhbjmYt3RKOCYiDhJ0vXAEa2cvxVpyYPewBzgjIjYVtIFwHGk5QUA+kTEaEm7Apfn635Mmlvta0qrEz4i6a58/nbA1hHR1PoaAIeTpp/ZBhgCPCrp/og4RNL7ETG6hZh/A9wXEYdJ6klKRq2StC5wGLB5RISkgRGxMCe0P0XEDfm8acDEiHhe0o7AfwJ75ttsBHyxHWtV1gE5uVh39FJEPJH3ZwIjWjn/nrxg23uS3gVuzeWzgK0rzrsG0oJMkgbkZLIvaYbof8zn9AY2zvtTW0gsADsD1+T/Sc+TdB/wedLkjq3Zk5T4yNe/W+Aa8nkfAZflms0nlqhWWgbii8Af0vyYAKxVccofnFjMycW6o48r9pcDa5PW4KhvJm687nzl+Ssqvq9g5X9DjSfqC0DAERHxXOWB/Nv+B22OvHoqf17IP3NELMtNensBRwLfpKFGUq8HsLCFmlOZP5d1EO5zMUteBrbP+0eu4j3q+0R2Jq0C+S5wJ/CtPAU+krZtw/0eAMZL6ilpKGn55UcKXjsN+Hp+Zk+l5ZsrvQJsIWmtXMPaK5/bD1gnIm4DTic1yQG8R5r1l7xo3UuSjsrXSNI2mFVwcjFLfgV8XdLjpP6NVfFRvv4ioH4q87OBNYCnJD2dvxd1M/AU8CRwN/CDNqyzcRqwh6RZpKa/lYYRR8SrwPXA7Pz5eD7UH/iT0nIDDwLfzeXXAt/PgxJGAscCJ0p6EniatAKm2d95yn0zM6s611zMzKzq3KFvVjJJnwOualT8cUTsWODaHwNHNSr+Q0T8v2rFZ7Yq3CxmZmZV52YxMzOrOicXMzOrOicXMzOrOicXMzOruv8PbXYYS0Q1iasAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(clusters, wcss, color='blue')\n",
    "plt.xlabel('number_of_cluster')\n",
    "plt.ylabel('sum of squared distance')\n",
    "plt.title('Elbow method')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f851f6a4",
   "metadata": {},
   "source": [
    "### After tuning, we get our optimal cluster number, k=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e113cfa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1,\n",
       "       0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 3, 4, 1, 4, 3, 4, 3, 4,\n",
       "       1, 4, 3, 4, 3, 4, 3, 4, 3, 4, 1, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4,\n",
       "       3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4,\n",
       "       3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4,\n",
       "       3, 4])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=5)\n",
    "pred = kmeans.fit_predict(df[['Income', 'Score']])   # fit_predict = fit() + predict() combination\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "237ec70e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 4, 3])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.clusters_number = pred    # column replace\n",
    "\n",
    "df.clusters_number.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "45ca12f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Score</th>\n",
       "      <th>clusters_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>46</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>79</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>86</td>\n",
       "      <td>95</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Age  Income  Score  clusters_number\n",
       "195       0   35     120     79                4\n",
       "58        0   27      46     51                1\n",
       "160       0   56      79     35                3\n",
       "167       0   33      86     95                4\n",
       "9         0   30      19     72                2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8fd08d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x175ab3b0f40>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Data visualization\n",
    "cl0 = df[df.clusters_number == 0 ]\n",
    "cl1 = df[df.clusters_number == 1 ]\n",
    "cl2 = df[df.clusters_number == 2 ]\n",
    "cl3 = df[df.clusters_number == 3 ]\n",
    "cl4 = df[df.clusters_number == 4 ]\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.scatter(cl0['Score'], cl0['Income'], color='green', label='cluster_0')\n",
    "plt.scatter(cl1['Score'], cl1['Income'], color='red',label='Cluster_1')\n",
    "plt.scatter(cl2['Score'], cl2['Income'], color='black',label='Cluster_2')\n",
    "plt.scatter(cl3['Score'], cl3['Income'], color='orange',label='Cluster_3')\n",
    "plt.scatter(cl4['Score'], cl4['Income'], color='cyan',label='Cluster_4')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "094a2989",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[26.30434783, 20.91304348],\n",
       "       [55.2962963 , 49.51851852],\n",
       "       [25.72727273, 79.36363636],\n",
       "       [88.2       , 17.11428571],\n",
       "       [86.53846154, 82.12820513]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "center = kmeans.cluster_centers_\n",
    "center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bf5f7812",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x175ab4478b0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Data visualization with centroids\n",
    "\n",
    "cl0 = df[df.clusters_number == 0 ]\n",
    "cl1 = df[df.clusters_number == 1 ]\n",
    "cl2 = df[df.clusters_number == 2 ]\n",
    "cl3 = df[df.clusters_number == 3 ]\n",
    "cl4 = df[df.clusters_number == 4 ]\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.scatter(cl0['Score'], cl0['Income'], color='green', label='cluster_0')\n",
    "plt.scatter(cl1['Score'], cl1['Income'], color='red',label='Cluster_1')\n",
    "plt.scatter(cl2['Score'], cl2['Income'], color='black',label='Cluster_2')\n",
    "plt.scatter(cl3['Score'], cl3['Income'], color='orange',label='Cluster_3')\n",
    "plt.scatter(cl4['Score'], cl4['Income'], color='cyan',label='Cluster_4')\n",
    "\n",
    "plt.scatter(center[:,0], center[:,1], color='purple', marker='+', label=\"centroid\")\n",
    "\n",
    "# certer[:,0] == means, all row-first column(Income) & center[:,1] == all row-2nd column(Score).\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a71db6d",
   "metadata": {},
   "source": [
    "### For exact cluster filtering, we can use kneed package to auto findout number_of_cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6513dd04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install kneed\n",
    "\n",
    "from kneed import KneeLocator\n",
    "kneedle = KneeLocator(clusters, wcss, curve='convex', direction='decreasing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4477bd6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<kneed.knee_locator.KneeLocator at 0x175ade00880>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kneedle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f33ba883",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "So, optimal number of cluster :  5\n"
     ]
    }
   ],
   "source": [
    "print(\"So, optimal number of cluster : \", kneedle.knee)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5530ccde",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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